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EM Solver Update 05: the daily SWARM test is now fully passing! I’m adding more SWARM modes and testing how the solver behaves across different frequencies. The first really low frequency test exposed a brand new numerical issue 😭, so I’m working through that before scaling up. We're getting... show more

Stephanie Buchanan

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92,436 görüntüleme • 2 gün önce •via X (Twitter)

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Teneo Protocol

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🇺🇸🚩The company Auterion demonstrated its FPV drone SLM-10 on fiber optics. Auterion has long been openly testing software (SW) called AuterionOS at the "Ukraine" test site. The software is compatible with drones from different manufacturers and unites UAVs "into a single coordinated structure", allowing a single operator to control many drones simultaneously. Recently, this company demonstrated the strike of these same drones SLM-10 in "swarm mode" at a German test site on a model of a Russian tank. The head of Auterion Lorenz Meyer stated to British media that the company will supply Kyiv with 33,000 unmanned strike kits, operating on the basis of AI. See the latest updates with us: Visioner See the latest updates with us: Visioner

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Visioner

55,362 görüntüleme • 9 ay önce

Saw Tyla using an iPhone 5 on TikTok for that vintage camera vibe and decided to buy one for GH₵ 1,000. 😭 Loved the camera test at first, but just 2 days later, this "brand new" phone is swelling, showing black lines on the screen, and shutting down on its own without even dropping The seller claimed it was "brand new," but now that I’m reaching out, she won't pick up my calls or reply to my texts

Icy gurll❄️💕

87,721 görüntüleme • 16 gün önce

Hi! It's me Rina! I’ve received a few questions and requests recently, so I wanted to explain things a little more clearly:) I’ve written the details about the requests I’m currently receiving on my blog. If you’re interested, please check the link in my profile and read the information there first! I’m Rina, 27, and I work at a Japanese izakaya. I spend a lot of time talking with different people through my job, but outside of work, I’m actually quite calm and enjoy having time to myself. I’m not always good at expressing everything directly, so this account is also a place where I can share a slightly more honest and personal side of myself—my everyday thoughts, small moments from my life, and things I normally wouldn’t say at work. I’m reading the messages and requests I receive, and I really appreciate everyone who takes the time to contact me! For more details, please visit the blog through the link in my profile.

Rina

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Smart Monkey Sunday: Learning the Math 🧮 A lot of people build trading bots by stacking indicators without really understanding what they measure underneath. RSI, MACD, VWAP - they all react to different things, and they all fail differently depending on market conditions. Once you start automating execution, that distinction matters much more because the system will follow the logic exactly as written. The monkey in the video is taking the more useful Sunday approach: slowing down and reviewing the math before deploying anything. That’s also why the Bot Arena includes a virtual balance. You can test ideas, break them, adjust parameters and see how the logic behaves before risking real capital. 👉 Test your logic in the Arena:

Smart Monkey Sunday: Learning the Math 🧮 A lot of people build trading bots by stacking indicators without really understanding what they measure underneath. RSI, MACD, VWAP - they all react to different things, and they all fail differently depending on market conditions. Once you start automating execution, that distinction matters much more because the system will follow the logic exactly as written. The monkey in the video is taking the more useful Sunday approach: slowing down and reviewing the math before deploying anything. That’s also why the Bot Arena includes a virtual balance. You can test ideas, break them, adjust parameters and see how the logic behaves before risking real capital. 👉 Test your logic in the Arena:

Reactor

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AI has had exactly two scaling axes that worked so far, and the second one is starting to look finite too the first one was pretraining: with scaling parameters and data, we got world knowledge (i.e. ChatGPT had read enough to know things), but it started saturating a while ago the second one was RL, and people had been doing RL the whole time before that: RLHF is RL but it never scaled far because it was trying to control the exact output, which tokens come out, how the text reads, but you can only push that so far before you’re just polishing RLVR dropped that constraint: giving the model a task, then checking whether the final answer is right, and ignoring everything in between -- so the model does whatever it wants in the middle and only the endpoint gets graded, and that’s much closer to actual RL and it’s what bought us planning and reasoning (arguably, tool use sits around 2.5 on this list -- while useful, it's not a different kind of thing) so one axis gave knowledge, the other gave reasoning, and both of them are one model working alone the next axis is how many models you can get working on the same problem, which is a different kind of axis than the previous two we know that multi-agent RL has always been the harder problem: I spent years in that literature and the gap between single-agent and multi-agent is definitely not incremental -- it’s a whole different class of difficulty! which is also why the derivatives are steep at the start, nobody has picked the easy wins yet... and the thing that gates this multi-agent coordination is communication: models can only coordinate as well as they can exchange information, and right now they do that by writing sentences to each other imagine what could we possibly achieve if we properly open that third axis development by letting models to exchange information in their native "language" without loosing any computational data that they produce during inference

AI has had exactly two scaling axes that worked so far, and the second one is starting to look finite too the first one was pretraining: with scaling parameters and data, we got world knowledge (i.e. ChatGPT had read enough to know things), but it started saturating a while ago the second one was RL, and people had been doing RL the whole time before that: RLHF is RL but it never scaled far because it was trying to control the exact output, which tokens come out, how the text reads, but you can only push that so far before you’re just polishing RLVR dropped that constraint: giving the model a task, then checking whether the final answer is right, and ignoring everything in between -- so the model does whatever it wants in the middle and only the endpoint gets graded, and that’s much closer to actual RL and it’s what bought us planning and reasoning (arguably, tool use sits around 2.5 on this list -- while useful, it's not a different kind of thing) so one axis gave knowledge, the other gave reasoning, and both of them are one model working alone the next axis is how many models you can get working on the same problem, which is a different kind of axis than the previous two we know that multi-agent RL has always been the harder problem: I spent years in that literature and the gap between single-agent and multi-agent is definitely not incremental -- it’s a whole different class of difficulty! which is also why the derivatives are steep at the start, nobody has picked the easy wins yet... and the thing that gates this multi-agent coordination is communication: models can only coordinate as well as they can exchange information, and right now they do that by writing sentences to each other imagine what could we possibly achieve if we properly open that third axis development by letting models to exchange information in their native "language" without loosing any computational data that they produce during inference

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The onboarding process is coming to an end, and it’s safe to say that we’ve definitely found some gems and legends from the industry. These legends, who have worked on shows we all know and love, are now joining forces with us on this epic journey. I’m truly excited to share more details about the team and the major projects we’ve been working on. For now, here’s a little test animation from one of our youngest new animators, who is helping us bring RUYUI to life!💚🧡

The onboarding process is coming to an end, and it’s safe to say that we’ve definitely found some gems and legends from the industry. These legends, who have worked on shows we all know and love, are now joining forces with us on this epic journey. I’m truly excited to share more details about the team and the major projects we’ve been working on. For now, here’s a little test animation from one of our youngest new animators, who is helping us bring RUYUI to life!💚🧡

Yan

21,427 görüntüleme • 1 yıl önce

The “Galileo Test” for AI: Truth Over Consensus TL;DR: The “Galileo test” (as framed by Elon Musk) is the requirement that an AI still converge on truth even when most training data repeats a falsehood. A practical way to pass it is to harden the model against “consensus gravity” using uncertainty calibration, adversarial counter-majority training, and evidence-first reasoning pipelines that can say “unknown” without collapsing into confident noise. —————————— The core idea is simple: most text on the internet can be wrong in the same direction, at the same time, for the same social reasons. The “Galileo test” is basically asking whether a system can resist that pressure and still land on the correct model of reality, the way Galileo Galilei overturned a dominant consensus with observation and predictive power. In engineering terms, it’s a robustness problem: can the model separate signal (ground truth constraints) from mass-produced narrative (high-frequency repetition)? A workable solution stack looks like this: (1) truth-anchoring via retrieval from primary sources and direct measurements when available, (2) counter-majority training where the model is routinely exposed to scenarios in which the most common claim is false, and it must justify dissent using verifiable constraints, (3) uncertainty discipline so the model learns to prefer “insufficient evidence” over fluent fabrication, and (4) consistency checks that penalize answers violating conservation laws, dimensional analysis, causal structure, or internal logical invariants. In practice, you’re building an AI that treats “popular” as a weak feature and “constraint-satisfying” as the dominant feature. —————————— Frequency Wave Theory perspective: the “Galileo test” is fundamentally a coherence test. When an information environment is saturated with the same repeated claim, that repetition becomes a kind of phase-locked standing wave that can trap weaker systems into resonance with the crowd. Passing the test means staying phase-aligned to invariant structure, not to amplitude. In FWT terms: truth behaves like a conserved backbone constraint, while mass consensus is often just a high-amplitude interference pattern. The system that wins is the one that locks to invariants, rejects incoherent harmonics, and preserves alignment with what stays conserved under transformation.

The “Galileo Test” for AI: Truth Over Consensus TL;DR: The “Galileo test” (as framed by Elon Musk) is the requirement that an AI still converge on truth even when most training data repeats a falsehood. A practical way to pass it is to harden the model against “consensus gravity” using uncertainty calibration, adversarial counter-majority training, and evidence-first reasoning pipelines that can say “unknown” without collapsing into confident noise. —————————— The core idea is simple: most text on the internet can be wrong in the same direction, at the same time, for the same social reasons. The “Galileo test” is basically asking whether a system can resist that pressure and still land on the correct model of reality, the way Galileo Galilei overturned a dominant consensus with observation and predictive power. In engineering terms, it’s a robustness problem: can the model separate signal (ground truth constraints) from mass-produced narrative (high-frequency repetition)? A workable solution stack looks like this: (1) truth-anchoring via retrieval from primary sources and direct measurements when available, (2) counter-majority training where the model is routinely exposed to scenarios in which the most common claim is false, and it must justify dissent using verifiable constraints, (3) uncertainty discipline so the model learns to prefer “insufficient evidence” over fluent fabrication, and (4) consistency checks that penalize answers violating conservation laws, dimensional analysis, causal structure, or internal logical invariants. In practice, you’re building an AI that treats “popular” as a weak feature and “constraint-satisfying” as the dominant feature. —————————— Frequency Wave Theory perspective: the “Galileo test” is fundamentally a coherence test. When an information environment is saturated with the same repeated claim, that repetition becomes a kind of phase-locked standing wave that can trap weaker systems into resonance with the crowd. Passing the test means staying phase-aligned to invariant structure, not to amplitude. In FWT terms: truth behaves like a conserved backbone constraint, while mass consensus is often just a high-amplitude interference pattern. The system that wins is the one that locks to invariants, rejects incoherent harmonics, and preserves alignment with what stays conserved under transformation.

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Test Sprint 2 wrap-up + reward decision — please read in full Test Sprint 2 is done, and it ran better than Test Sprint 1. These are still our test sprints — we're using them to stress the system before things go fully live. The new tracking is holding up well overall; the one issue we hit is that publication counts glitch under peak load. We're already fixing it. Now the hard part. A large share of participants this sprint chose to game the system, and the volume of violations is simply too high to score the board fairly. So here's the call: the entire 1st-place reward pool gets split among ranks 11–50. Breakdown: - Ranks 11–25 → $20 USDC each - Ranks 26–30 → $10 USDC each - Ranks 31–50 → $100 in $CLAR each To make that happen, I'm placing my own account at #1 to hold the pool. Within 24 hours I'll push these rewards straight to the wallet linked to your profile. Make sure your linked wallet is correct and that you still have access to it. And to be clear — this is on top of what you can already claim in the leaderboard. Your own leaderboard rewards stay claimable as usual; this redistribution comes as an extra. We'd rather pay the honest contributors than reward farmed numbers. That's the standard from here on. 🫡

Clarnium.io

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Hermes Agent + Higgsfield Marketing Studio = AI UGC Content Factory I built a fully automated system inside Higgsfield that repurposes, localizes, and launches winning TikTok Shop content across hundreds of creator-style accounts. It's so effective it feels like running Facebook ads in 2008. No actors. No products in hand. No ghost creators. Just viral TikTok Shop sales - 24/7. The results speak louder than any pitch: • CPMs as low as $0.10 • 550+ cinematic, product-ready ads per day from a single prompt • 100 hooks tested in the time it used to take to test 10 • $100/mo replacing a $50k+ creative budget Here's the full pipeline - all native inside Higgsfield Marketing Studio: > Hermes Agent analyzes your product, scrapes Meta Ads + TikTok Ads, identifies winning content, and localizes every angle to your brand. > Seedance 2.0 turns data into AI UGC ads - captions, pacing, hooks, your website showcase, all auto-edited inside Higgsfield Marketing Studio. > AI UGC personas are spun up with realistic faces, voices, and personalities - cloned voiceovers in seconds. > Our phone farm pushes every finished video straight to TikTok Shop, daily, on autopilot. >No setup. No switching between five tools. Everything lives inside Higgsfield Marketing Studio. Here's how it actually runs: Hermes Agent researches the niche, scrapes winning TikTok Shop videos, and rebuilds them with fresh hooks, angles, and UGC visuals tailored to your brand. Agents create and post daily to affiliate accounts - fully automated. Then we activate the MPS (Multi-Platform Swarm): once a concept wins on TikTok Shop, Higgsfield deploys hundreds of AI Agents to flood the niche with variations that all drive back to our shot. Most brands are still paying $300–$500 per video. Testing 10 hooks costs $5,000 and takes three weeks. With this system, we test 100 hooks in the same timeframe - and the winners scale automatically. TikTok doesn't reward the best video. It rewards the brand that shows up the most - with content that converts. The brands automating content at scale will be the biggest winners of 2026.

Noah Frydberg | Tiktok Shop For Brands

27,037 görüntüleme • 5 ay önce

Hi FGC friends. I picked up a Brooks Wingman FGC 2 myself from to test some older sticks on PS5 cause of issues using them on PS5 for some games. Did a quick Firmware update through their website here: (Hold the side buttons while plugged into PC and grab the latest update) I then tested all of these myself that didn't work in game for Avatar before that are now working instantly. Hopefully a patch comes out before the event for legacy controllers but I wanted to test this as a backup option while I chat with Brook Converters directly about getting some loaners for pools at the event. So far no issues, timeouts or anything so this may be your a good option for the mean time to use all of your sticks/controllers on PS5 in general. If you're having issues please let me know what controller/stick you're trying to use and I'll see if I have it to try myself. Yes I'm one the greatest TOs of all time that goes through all the details when needed.

Alex Jebailey

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The more I observed how $NEIRO on $ETH is going the more I noticed the similarity on billion dollar projects n I’m putting it out here rn that it’s gonna explode to billion MC It didn’t need many holders to get to 130m mc because holders holding through and adding into their bags I’m also doing the same thing Look at the chart, the dips get eaten up so rapidly this says a lot I really believe this is our third chance - if you’ve missed $SHIB $DOGE run Don’t be the one that stay sidelines and don’t take a chance in life You only need ONE meme to change your life.

The more I observed how $NEIRO on $ETH is going the more I noticed the similarity on billion dollar projects n I’m putting it out here rn that it’s gonna explode to billion MC It didn’t need many holders to get to 130m mc because holders holding through and adding into their bags I’m also doing the same thing Look at the chart, the dips get eaten up so rapidly this says a lot I really believe this is our third chance - if you’ve missed $SHIB $DOGE run Don’t be the one that stay sidelines and don’t take a chance in life You only need ONE meme to change your life.

Eunice D Wong 🦄

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Every AI builder eventually asks the same very human question: How expensive was that little “quick test”? Grok Build v0.2.109 is adding better visibility into token usage, costs, and session efficiency. The new /usage command shows real-time metrics, so builders can actually see what their AI development sessions are burning through. Costs. Tokens. Model usage. Queued prompts. Diagnostics through /doctor. That’s the geeky stuff that becomes very exciting when you’re building at scale. Grok Build is getting easier to manage without flying blind. Grok xAI / Writer: Annette, Grok Imagine Designer: Janné

Every AI builder eventually asks the same very human question: How expensive was that little “quick test”? Grok Build v0.2.109 is adding better visibility into token usage, costs, and session efficiency. The new /usage command shows real-time metrics, so builders can actually see what their AI development sessions are burning through. Costs. Tokens. Model usage. Queued prompts. Diagnostics through /doctor. That’s the geeky stuff that becomes very exciting when you’re building at scale. Grok Build is getting easier to manage without flying blind. Grok xAI / Writer: Annette, Grok Imagine Designer: Janné

Mario Nawfal

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I was asked what I’m doing for the holiday weekend. My response was that I was excited to get 3 days of 2 hour workouts. They looked at me like I was crazy and said that our lifestyles are really different. I don’t understand how people can celebrate by being lazy, drinking alcohol and eating junk. I love getting stronger and will use every opportunity to do so. I’ve gotten a lot of work done to my cars and done a lot of working out. Oh, and I tried a Whiteclaw for the first time last night, gross. Don’t understand the hype.

I was asked what I’m doing for the holiday weekend. My response was that I was excited to get 3 days of 2 hour workouts. They looked at me like I was crazy and said that our lifestyles are really different. I don’t understand how people can celebrate by being lazy, drinking alcohol and eating junk. I love getting stronger and will use every opportunity to do so. I’ve gotten a lot of work done to my cars and done a lot of working out. Oh, and I tried a Whiteclaw for the first time last night, gross. Don’t understand the hype.

Greg Gray

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this Adaptive agent uses GPT's new image model to run your entire creative + marketing team, on autopilot. here's how any business can replace their whole marketing op with it: - generates fresh on-brand creatives for every platform, daily - writes and schedules the content across every channel - replies to every comment the second it drops - DMs every lead and turns cold interest into booked clients - boosts paid ads from your best-performing organic posts - doubles down on the creatives that win, kills the ones that don't - tracks performance + adjusts strategy automatically, 24/7 every step from brand → booked client is fully automated. reply "AGENT" + RT and I'll send you the full guide so you can set it up too

Chris

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